Abstract. Predicting accurately and in real-time 3D body joint positions from a depth image is the cornerstone for many safety, biomedical, and entertainment applications. Despite the high quality of the depth images, the accuracy of existing human pose estimation methods from single depth images remains insufficient for some applications. In order to enhance the accuracy, we suggest to leverage a rough orientation estimation to dynamically select a 3D joint position prediction model specialized for this orientation. This orientation estimation can be obtained in real-time either from the image itself, or from any other clue like tracking. We demonstrate the merits of this general principle on a pose estimation method similar to the one used with Kinect cameras. Our results show that the accuracy is improved by up to 45.1 %, with respect to a method using the same model for all orientations.

Reliable measurements of feet trajectories are needed in some applications, such as biomedical applications. This paper describes the data processing pipeline used in GAIMS, which is a non-intrusive system that measures feet trajectories based on multiple range laser scanners. Our processing pipeline relies on a new tracking paradigm, and it is based on two innovative algorithms: the first algorithm localizes the feet directly from the observed point cloud without any clustering, and the other algorithm identifies the feet. After reviewing the various types of noise affecting the point cloud, this paper explains the limitations of the classical processing approach and gives an overview of our new pipeline. The effectiveness of the proposed approach is established by discussing the results that have been obtained in several studies based on GAIMS.

Abstract. In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we first estimate the orientation of the standing person seen by the camera and then use this information to dynamically select a pose estimation model suited for this particular orientation. We evaluated our method on a public dataset of realistic depth images with precise ground truth joints location. Our experiments show that our method decreases the error of a state-of-the-art pose estimation method by 30%, or reduces the size of the needed learning set by a factor larger than 10.